dingdingcai/OVE6D-pose

OVE6D: Object Viewpoint Encoding for Depth-based 6D Object Pose Estimation (CVPR 2022)

• Paper
• Project page
• Another good implementation of this project can be found here with real demos.

@inproceedings{cai2022ove6d,
  title={OVE6D: Object Viewpoint Encoding for Depth-based 6D Object Pose Estimation},
  author={Cai, Dingding and Heikkil{\"a}, Janne and Rahtu, Esa},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={6803--6813},
  year={2022}
}

Setup

Please start by installing Miniconda3 with Pyhton3.8 or above.

Denpendencies

This project requires the evaluation code from bop_toolkit and sixd_toolkit.

Dataset

Our evaluation is conducted on three datasets all downloaded from BOP website. All three datasets are stored in the same directory. e.g. Dataspace/lm, Dataspace/lmo, Dataspace/tless.

Quantitative Evaluation

Evaluation on the LineMOD and Occluded LineMOD datasets with instance segmentation (Mask-RCNN) network (entire pipeline i.e. instance segmentation + pose estimation)

python LM_RCNN_OVE6D_pipeline.py for LineMOD.

python LMO_RCNN_OVE6D_pipeline.py for Occluded LineMOD.

Evaluation on the T-LESS dataset with the provided object segmentation masks (downloaded from Multi-Path Encoder).

python TLESS_eval_sixd17.py for TLESS.

Training

To train OVE6D, the ShapeNet dataset is required. You need to first pre-process ShapeNet with the provided script in training/preprocess_shapenet.py, and Blender is required for this task. More details refer to LatentFusion.

pre-trained weight for OVE6D

Our pre-trained OVE6D weights can be found here. Please download and save to the directory checkpoints/.

Segmentation Masks

• 1. For T-LESS we use the segmentation masks provided by Multi-Path Encoder.
• 2. For LineMOD and Occluded LineMOD, we fine-tuned the Mask-RCNN initialized with the weights from Detectron2. The training data can be downloaded from BOP.

Acknowledgement

• 1. The code is partially based on LatentFusion.
• 2. The evaluation code is based on bop_toolkit and sixd_toolkit.